Diagnostic apparatus

ABSTRACT

A diagnostic apparatus ( 10 ) for analysing a sample to diagnose disease, the apparatus ( 10 ) comprising: a separating element ( 16 ) for separating gas derived from the sample into component parts; a sensor arrangement ( 18 ) coupled to the separating element ( 16 ) such that a component part of the gas is directed towards the sensor arrangement ( 18 ), the sensor arrangement ( 18 ) being configured to detect compounds which may be indicative of disease; and a processing element ( 20 ) coupled to an output of the sensor arrangement ( 18 ), the processing element ( 20 ) being configured to process a signal output by the sensor arrangement ( 18 ) to provide a diagnosis.

The present application relates to a diagnostic apparatus for analysinga sample to diagnose disease, and to a method of diagnosing diseaseusing such an apparatus.

Infectious diseases such as Clostridium difficile (C. difficile),Norovirus, cholera and Campylobacter can spread rapidly, causingdistress and even morbidity to those affected and considerabledifficulty and expense to public health authorities treating patientsand attempting to manage outbreaks of such diseases. A major problem inoutbreak management is that there is often a delay between diseasesymptoms being presented and a diagnosis being made, due totime-consuming diagnostic techniques requiring manual laboratoryanalysis of samples, for example stool samples, provided by patients. Insome cases the results of such analysis may not be available for up tofive days, leading to unnecessarily long delays in diagnosing thepatient. Until an accurate diagnosis is made, effective treatment of thepatient can be difficult, which can lead to deterioration in thepatient's condition and prolonged and unnecessary suffering to thepatient. Additionally, whilst the patient is awaiting diagnosis, thedisease is able to spread through contact with the patient by medicalstaff, relatives and the like, or by airborne transmission. Thus, delaysin diagnosing infectious diseases can lead to a widespread outbreak ofdisease.

Non-infectious diseases can also be diagnosed by analysis of a sampleprovided by a patient. For example, prostate cancer is usually diagnosedby a combination of techniques such as digital rectal examination, atest for serum prostate specific antigen (PSA) and trans-rectalultrasound-guided prostate biopsy. Worldwide, the use of serum levels ofPSA as a screening test remains controversial due to its low specificity(38%) and the acknowledged high rate of false negative results (up to20% with PSA level <4 ng ml⁻¹). Digital rectal examination andtrans-rectal ultrasound-guided prostate biopsy are invasive procedureswhich are stressful to the subject and may deter patients frompresenting for testing.

In view of this, there is a need for a highly sensitive, specific,non-invasive and cost-effective diagnostic system for diagnosing diseasequickly and easily at the point of care of the patient.

Efforts have been made to develop diagnostic techniques whereby diseasecan be diagnosed quickly and accurately by analysing samples such asstool samples, urine samples or the like via analysis of volatilebiomarkers. However, these efforts have not been successful to date andhave been hampered by technical difficulties such as the high moisturecontent of samples and the presence in the samples of sulphides whichare damaging to sensors. More important, however, is the difficulty inselecting how to analyse the samples. For example, it has been shownexperimentally that stool samples can contain a large number ofdifferent volatile organic compounds (VOCs); across a cohort study ofstool samples provided by 30 donors, 297 different VOCs were identifiedin the stool samples. Determining which of these compounds andsubstances could be indicative of disease represents a major challengewhich has hitherto not been overcome.

According to a first aspect of the invention there is provided adiagnostic apparatus for analysing a sample to diagnose disease, theapparatus comprising: a separating element for separating gas derivedfrom the sample into component parts; a sensor arrangement coupled tothe separating element such that a component part of the gas is directedtowards the sensor arrangement, the sensor arrangement being configuredto detect a compound which may be indicative of disease; and aprocessing element coupled to an output of the sensor arrangement, theprocessing element being configured to process a signal output by thesensor arrangement to provide a diagnosis.

The diagnostic apparatus of the present invention permits rapid andaccurate diagnosis by detecting compounds or groups of compounds presentin the sample which are indicative of disease. The apparatus can beprovided as a stand alone device which can be installed in hospitals,doctors' surgeries and other medical facilities to permit fast, accuratediagnosis at the point of care, allowing a doctor, nurse or othermedical practitioner to begin effective treatment quickly and to put inplace any measures which may be necessary to prevent or restrict thespread of disease. The sample may be a sample of a bodily fluid such asurine or saliva, or may be a sample of a solid or semi-solid such asfeces. Alternatively or additionally the sample may comprise a gasevolved from a liquid or solid such as urine or feces.

The separating element may comprise a multi-capillary column.

Alternatively or additionally, the separating element may comprise asingle-capillary column, or may comprise a plurality of single-capillarycolumns.

The sensor arrangement may comprise one or more sensors selected fromthe group comprising a metal-oxide sensor, a UV sensor and an ammonia oramine sensor.

In this context, the term “metal oxide sensor” refers to a sensor whichuses a heated metal oxide element to detect certain volatile compounds,whilst the term “UV sensor” refers to a sensor which uses an ultravioletor near ultraviolet light activated metal oxide element to detectcertain volatile compounds. The term “ammonia or amine sensor” refers toa sensor that detects ammonia or amines.

Preferably the sensor arrangement comprises two or more sensors arrangedin a serial configuration.

Alternatively, the sensor arrangement may comprise two or more sensorsarranged in a parallel configuration.

The sensor arrangement may be configured to detect one or more volatilecompounds present in the gas. For example, the sensor arrangement may beconfigured to detect one or more volatile organic compounds in the gas.

The sensor arrangement may be configured to generate a signal indicativeof the elution time of a volatile compound in the sample.

The processing element may be configured to compare the signal generatedby the sensor arrangement to a known profile from one or morepreviously-diagnosed samples.

The apparatus may further comprise a pre-treatment stage for alteringphysio-chemical parameters of the sample.

The apparatus may further comprise heating means for heating the sampleto promote the release of the gas.

The apparatus may further comprise means for acidifying or basifying thesample, to alter the number or concentration of volatile compoundsdetected by sensor arrangement.

The processing element may implement an artificial neural network toprovide the diagnosis.

According to a second aspect of the invention there is provided a methodof diagnosing disease by analysing a sample, the method comprising thesteps of: collecting the sample; separating a gas derived from thesample into component parts; directing a component part of the gastowards a sensor arrangement, the sensor arrangement being configured todetect a compound which may be indicative of disease; and processing asignal output by the sensor arrangement to provide a diagnosis.

The step of separating the gas may comprise passing the gas through aseparating element comprising a multi-capillary column.

Alternatively, the step of separating the gas may comprise passing thegas through a separating element comprising a single-capillary column ora plurality of single-capillary columns.

The sensor arrangement may comprise one or more sensors selected fromthe group comprising a metal-oxide sensor, a UV sensor and an ammonia oramine sensor.

Preferably, the sensor arrangement comprises two or more sensorsarranged in a serial configuration.

Alternatively, the sensor arrangement may comprise two or more sensorsarranged in a parallel configuration.

The sensor arrangement may be configured to detect one or more volatilecompounds present in the gas.

The sensor arrangement may be configured to detect one or more volatileorganic compounds present in the gas.

The sensor arrangement may be configured to generate a signal indicativeof the elution time of a volatile compound in the sample.

The step of processing the signal output by the sensor arrangement maycomprise comparing the signal generated by the sensor arrangement to aknown profile from one or more previously-diagnosed samples.

The method may further comprise pre-treating the sample to alterphysio-chemical parameters of the sample.

The method may further comprise heating the sample to promote therelease of the gas.

The method may further comprise acidifying or basifying the sample toalter the number or concentration of volatile compounds detected bysensor arrangement.

The step of processing the signal output by the sensor arrangement maycomprise processing the signal using an artificial neural network toprovide the diagnosis.

According to a third aspect of the invention there is provided anammonia or amine sensor comprising a light source which emits light inthe visible range and a photodetector, the light source being arrangedto emit light towards a detecting surface of the photodetector inoperation of the ammonia or amine sensor, wherein an ammonia- oramine-sensitive substance having an optical property which changes inthe presence of ammonia or an amine is disposed in an optical pathbetween the light source and the detecting surface.

One of the detecting surface and the light source may be at leastpartially coated in the ammonia- or amine-sensitive substance.

Alternatively, the ammonia or amine sensor may further comprise asubstantially transparent medium which is at least partially coated inthe ammonia- or amine-sensitive substance, the substantially transparentmedium being disposed in the optical path between the light source andthe detecting surface of the photodetector.

The optical transmissivity of the ammonia- or amine-sensitive substancemay decrease in the presence of ammonia

The ammonia- or amine-sensitive substance may comprise a pH-sensitivedye in a solution mixed with a polymer material.

The pH-sensitive dye may be bromophenol blue.

The polymer material may comprise polyvinylpyrrolidone, for example.

The light source may be selected so as to have a peak wavelength whichfalls within a main pass band of the ammonia- or amine-sensitivesubstance in the absence of ammonia.

For example, the light source may have a peak wavelength of around 602nm.

The light source may comprise an LED.

The photodetector may comprise a photodiode.

The ammonia or amine sensor may further comprise a second light source,the second light source being selected so as to have a peak wavelengthwhich falls within a main pass band of the ammonia- or amine-sensitivesubstance in the presence of ammonia or amine.

For example, the second light source may have a peak wavelength ofaround 432 nm.

The light source and the second light source may be arranged to beactuated in an alternating manner.

The second light source may be arranged to emit light towards thedetection surface of the photodetector in operation of the ammonia oramine sensor.

Embodiments of the invention will now be described, strictly by way ofexample only, with reference to the accompanying drawings, of which:

FIG. 1 is a schematic representation of a diagnostic apparatus;

FIG. 2 is a schematic representation of an embodiment of a detectionstage of the diagnostic apparatus of FIG. 1;

FIG. 3 is a schematic representation of an alternative embodiment of adetection stage of the diagnostic apparatus of FIG. 1;

FIG. 4 is a schematic representation of an ammonia or amine sensor usedin certain embodiments of the diagnostic apparatus shown in FIG. 1;

FIG. 5 is a schematic representation of an alternative embodiment of anammonia or amine sensor used in certain embodiments of the diagnosticapparatus shown in FIG. 1;

FIG. 6 is a schematic representation of a heated metal oxide sensor usedin certain embodiments of the diagnostic apparatus shown in FIG. 1;

FIG. 7 is a flow diagram illustrating a process for acquiring data totrain an artificial neural network used in certain embodiments of theapparatus shown in FIG. 1;

FIG. 8 is a flow diagram illustrating a process used to produce databins for use in the artificial neural network used in certainembodiments of the apparatus shown in FIG. 1;

FIG. 9 is a flow diagram illustrating a training process for theartificial neural network used in certain embodiments of the apparatusof FIG. 1;

FIG. 10 is a flow diagram illustrating a process used by the artificialneural network used in certain embodiments of the apparatus of FIG. 1 tomake a real-time diagnosis of a disease;

FIG. 11 is a graph showing the results of a discriminant analysisperformed on the results of analysis of stool samples using a prototypediagnostic apparatus;

FIG. 12 is a screen shot showing the results of an analysis of a freshuntreated urine sample produced using a prototype diagnostic apparatus;and

FIG. 13 is a screen shot showing the results of an analysis of anacidified urine sample produced using the prototype diagnosticapparatus.

Referring first to FIG. 1, a diagnostic apparatus is shown in schematicform at 10. The apparatus 10 has a gas chromatography oven 12 with aninlet port 14 through which gas evolved from a sample of a bodily fluidsupplied by a patient can be injected into an inlet of one or moreseparating columns housed in the gas chromatography oven 12. In someembodiments the inlet port 14 communicates directly with an outlet of acontainer in which the sample is stored and in which the sample may beheated, to a temperature of around 60° C. for example (although othertemperatures may also be suitable), to release the gas; whilst in otherembodiments the storage and collection of gas is performed elsewhere,for example in a headspace vial with an integrated septum from which gascan be collected in a syringe and subsequently injected or pumped intothe gas chromatography oven 12 through the inlet port 14.

It has been found that embodiments in which the inlet port 14communicates directly with the outlet of the sample container gives riseto significant improvements in performance compared to embodiments inwhich the gas is injected or pumped into the gas chromatography oven 12through the inlet port.

This is because in these “closed loop” embodiments a series of valvescan be used to extract the gas evolved from the sample using clean air.The gas collected in this way can then be directed to a separation stage16, which is described in more detail below. Moreover, in closed loopembodiments in which the inlet port 14 communicates directly with theoutlet of the sample container there is less scope for loss of thesample, as the sample is provided directly to the inlet port rather thanbeing drawn into a syringe, which is typically at room temperature,before being transferred to the inlet port 14 of the gas chromatographyoven 12. Additionally, in the “closed loop” embodiments where the inletport 14 communicates directly with the outlet of the sample containerall of the components of the sampling system can be maintained at hightemperatures (e.g. around 90-100° C.).

In some embodiments of the apparatus 10, a pre-treatment stage 11 may beprovided, to alter the psysio-chemical parameters of the sample tooptimise the relative concentrations of volatile compounds in the gasevolved from the sample for diagnostic purposes. For example, in thepre-treatment stage 11 volatile compounds from the gas evolved from thesample may be collected in one or more solid-phase microextration (SPME)fibres to concentrate the volatile compounds, which fibre(s) are thenintroduced into the inlet port 14 and heated to cause the organiccompounds collected in the SPME fibre(s) to desorb from the fibre(s).Gas evolved from the sample may also be collected and stored usingAutomated Thermal Desorption tubes may be similarly introduced into theinlet port 14. Additionally or alternatively, the pre-treatment stage 11may include a heated water bath or other heating means to heat thesample to promote or accelerate the release of gas. The pre-treatmentstage 11 may include means for acidifying or basifying the sample priorto collecting the gas, to alter the number and/or concentration ofvolatile compounds evolved from the sample. For example, thepre-treatment stage 11 may include one or more injectors for injecting apredetermined quantity of an acid (e.g. sulphuric acid) or a base (e.g.sodium hydroxide) into the container in which the sample is stored. Thepre-treatment stage 11 may be integral with other stages of apparatus10, or may be provided as a dedicated device which is separate from theother stages of the apparatus 10. Moreover, the pre-treatment stage 11may have a number of discrete sub-stages, for example a heatingsub-stage, a concentration stage and an acidification or basificationstage.

The apparatus 10 may include a pump 30 which supplies an air flow tocarry the gas around the apparatus 10. In the embodiment illustrated inFIG. 1 the pump 30 supplies air from an exterior of the apparatus 10.The applicant has found that sensors used in the apparatus 10 (whichsensors are described in detail below) function optimally in air.Additionally, air is readily available and requires no bulky cylindersfor storage. To prevent airborne volatile compounds and othercontaminants from the environment in which the apparatus 10 is installedfrom interfering with the operation of the apparatus 10 a filter such asa charcoal filter or an activated charcoal filter is provided at the oreach outlet of the pump 30 of this embodiment such that the air pumpedaround the apparatus 10 is filtered prior to exiting the pump 30. Theflow rate of the filtered air may be around 200 ml/minute. It will beappreciated, of course, that other types of filter may be employed forthe purpose of filtering the air. In other embodiments the pump 30 mayinclude an inlet through which a carrier gas such as purified helium maybe introduced, which carrier gas may carry the gas evolved from thesample around the apparatus 10.

An inlet 14 of the gas chromatography oven 12 communicates with aseparating stage 16 which is made up of one or more separating columns.In the embodiment illustrated in FIG. 1 the separating stage 16 ishoused within the oven 12. This arrangement provides a stabletemperature for the separating stage 16 so that elution times forvolatile compounds contained in the gas evolved from the sample remainthe same regardless of any differences in the temperature of theenvironment in which the apparatus 10 is operating. The inlet 14 of theseparating stage may be contained within the oven 12, or may be externalto the oven 12. The temperature of the inlet 14 is independent of thetemperature of the oven 12, and may be adjusted independently of thetemperature of the gas chromatography oven 12. The applicant has foundthat an inlet temperature of 100° C. and an oven temperature of between30° C.-40° C. produce good results.

In one embodiment two 30 metre capillary columns are used in theseparating stage 16, with the outlet of each column being divided intotwo such that the separated components of the gas arrive simultaneouslyat four outlets.

In an alternative embodiment a multi-capillary column having a length ofaround 0.5 metres is used in the separating stage 16. It will beappreciated, however, that different lengths and configurations ofcolumn can be used, and indeed a plurality of multi-capillary columnscan be used. For example, the separating stage may include asingle-capillary column or a plurality of single-capillary columns ofthe same or different lengths.

The multi-capillary column used in this example may have around 1200separate capillaries. An advantage of using such a multi-capillarycolumn in the separating stage is that it is capable of quicklyseparating volatile compounds such as volatile organic compounds in thegas. For example, volatile compounds from stool samples can be separatedin around 5 minutes. The multi-capillary column can also be used at roomtemperature, whilst its small size is advantageous as it allows theapparatus 10 to be small and portable such that it can easily beaccommodated at a point of patient care such as a doctors' surgery,clinic or the like.

The separation stage 16 may be varied to change its separationcharacteristics, for example to change the elution time of certainvolatile compounds. An inside wall of the capillaries of the separationstage 16 (whether single capillary columns, as used in the firstembodiment described above, or a multi-capillary column, as used in thealternative embodiment described above) is coated with a thin layer of astationary phase, and the thickness and chemical properties of thisstationary phase coating affect the separating capabilities of thecapillary, for example by altering the elution time of certaincompounds. In certain applications of the apparatus 10 it may bebeneficial to use a plurality of capillaries, each having a differentinner stationary phase coating, in the capillaries of the separatingstage 16, to improve the differentiating ability of the apparatus 10,thereby improving the capability of the apparatus 10 to diagnose diseaseaccurately.

The outlet(s) of the separating stage 16 are coupled to one or moreinlets of a detection stage 18 which is configured to detect one or morevolatile compounds present in the gas derived from the sample. Thedetection stage 18 includes one or more sensors for sensing particularvolatile compounds or groups of volatile compounds, as is discussed inmore detail below. In one embodiment an array of sensors is provided,the array containing one or more of each of a metal-oxide sensor, a UVsensor and an ammonia or amine sensor.

The sensor(s) of the detection stage 18 may be housed in a containerwhich is positioned inside the gas chromatography oven 12. The containermay be made of aluminium, or any other suitable material. The containershould be electrically isolated from electronic components of thesensor(s) of the detection stage 18 to avoid any risk of unwantedelectrical connections between electronic components. Housing thesensor(s) of the detection stage 18 in a separate container in this wayensures that the sensor(s) are maintained in constant environmentalconditions, which helps to improve the response and stability of thesensor(s). Additionally, the container in which the sensor(s) of thedetection stage 18 are housed helps to isolate the sensors frompotentially interfering volatiles from other components of the apparatus10.

The sensor(s) of the detection stage 18 produce electrical outputs whichchange when a volatile compound is detected. The outputs of thesensor(s) of the detection stage 18 are connected to a processor 20which is operative to interpret the output(s) of the sensor(s) todetermine whether volatile compounds which are indicative of disease arepresent in the gas derived from the sample, and to provide a diagnosisof the disease on a display 22 of the apparatus 10. The operation of theprocessor 20 is described in more detail below.

The operation of the apparatus 10 is controlled by a control system 24,and the apparatus 10 may have a storage device 26 such as a hard discdrive, non-volatile memory or an optical storage device such as a CD orDVD recorder or the like for storing and retrieving different diagnosticprograms (as is discussed in more detail below) and for storingdiagnoses and other results produced by the apparatus 10. Alternatively,the diagnostic programs, diagnoses and other results may be stored inmemory in the processor 20, the control system 24 or both. Suggestedtreatment plans may also be stored in the storage device 26, processor20 or control system, each treatment plan being associated with aparticular diagnosis such that on successful diagnosis by the apparatus10 a suggested treatment plan is provided with the diagnosis.

FIG. 2 is a schematic representation of a detection stage 18 used in anembodiment of the apparatus 10. In this embodiment the separation stage16 has a single outlet which communicates with an inlet 40 of thedetection stage 18 through which volatile compounds from the gas derivedfrom the sample are directed to a sensor array, which is shown in dottedoutline at 42 in FIG. 2. The sensor array 42 in this example includesfour sensors: an ammonia or amine sensor 44 (of a type described in moredetail below), a sensor 46 (of a type that will be familiar to thoseskilled in the art) which uses an ultraviolet (or near ultraviolet)light activated metal oxide element at room temperature to detectcertain volatile compounds (hereinafter referred to as a UV sensor) andsensors 48, 50 of a type described below which use a heated metal oxideelement to detect certain volatile compounds (hereinafter referred to asheated metal oxide sensors 48, 50). It will be appreciated, however,that other gas sensors could be used in the sensor array 42 of theapparatus 10, alongside or in place of one or more of the sensors 44,46, 48, 50.

The sensors 44, 46, 48, 50 are arranged in a serial configuration suchthat volatile compounds passing from the inlet 40 to an outlet 52 of thedetection stage pass each sensor 44, 46, 48, 50 in sequence. As isdescribed in more detail below, the heated metal oxide sensors 48, 50include heaters which heat the sensors 48, 50 to temperatures in therange of 300° C. 600° C. These temperatures are sufficient to destroyany viruses or other microbiological contaminants that may be present inthe volatile compounds entering the detection stage 18. Thus, placingthese sensors at the end of the flow path for the volatile compounds inthe detection stage 18 ensures that any viruses present in the volatilecompounds are destroyed before any exhaust gas is exhausted through theoutlet 52 of the detection stage 18.

The sensors 44, 46, 48, 50 each have a control input 54 through whichcontrol signals from the control system 24 can be received to controlthe operation of the sensors 44, 46, 48, 50 and a signal output 56 fortransmitting output signals to the processor 20 and control system 24.The output signals may be conditioned by a pre-processing stage, or maybe transmitted directly to the processor 20 for processing. The metaloxide sensors 48, 50 also have temperature control signal inputs 58 bymeans of which the control system 24 can control the temperature of thesensor 48, 50, whilst temperature measurement outputs 60 provide signalsindicative of the temperature of the sensors 48, 50 to the controlsystem 24.

An alternative arrangement of the detection stage 18 is illustrated inFIG. 3, in which the same reference numerals are used to identifyelements common to the embodiments of FIGS. 2 and 3. In the arrangementshown in FIG. 3, the separating stage 16 has four output columns whicheach communicate with a respective inlet 62 of the detection stage 18.The inlets 62 are each coupled to a respective one of the sensors 44,46, 48, 50 in a parallel configuration. The arrangement of FIG. 3suffers from a reduction in sensitivity in comparison to that shown inFIG. 2, as the flow of volatile compounds is split, whilst there is anincreased risk that viruses and the like present in the volatilecompounds may not be destroyed prior to being exhausted through theexhaust port 52, as not all of the volatile compound streams pass overthe heated metal oxide sensors 48, 50.

An ammonia or amine sensor 44 is illustrated schematically in FIG. 4,and comprises a visible light source 70, which in this example is anorange light emitting diode (LED) having a peak wavelength of 602 nm,and a photodetector 72, which in this example is an amplifiedphotodiode. The LED 70 and photodiode 72 are arranged such that lightemitted by the LED is directed towards a detecting surface of thephotodiode 72. An ammonia- or amine-sensitive material 74, which in thisexample is an ammonia- or amine-sensitive dye film consisting of apH-sensitive dye such as bromophenol blue in a solution mixed withpolyvinylpyrrolidone, is disposed between the LED 70 and the detectingsurface of the photodiode 72. The ammonia- or amine-sensitive dye film74 may be deposited directly onto one or both of the LED 70 and thedetecting surface of the photodiode 72, or may be provided on asubstantially transparent medium 76 disposed in an optical path betweenthe LED 70 and the detecting surface of the photodiode 72.

In operation of the ammonia or amine sensor 44, a control signal isreceived from the control system 54 to actuate the light source 70. Thephotodetector 72 detects the light from the light source 70 and outputsa voltage in the range 0 to 2.5V, which output voltage is dependent onthe intensity of light received by the photodetector 72. In thisexample, when no ammonia or amine is present, the ammonia- oramine-sensitive material 74 has a peak transmittivity at 606 nm, andthus allows substantially all the light from the light source 70 to passto the photodetector 72, such that the photodetector produces a highoutput voltage (e.g. at or close to 2.5V). However, where ammonia oramine is present, the optical transmittivity at 606 nm of the ammonia-or amine-sensitive material 74 is reduced, having in this example a peaktransmittivity at 432 nm. Thus, the intensity of light received by thephotodetector 72 is reduced and the output voltage of the photodetector72 is reduced. The output of the photodetector 72 is passed to theprocessor 20 (with or without pre-processing) which processes the outputsignal in conjunction with signals output by the other sensors 46, 48,50 to determine if volatile compounds present in the gas derived fromthe sample supplied by the patient are indicative of disease. It will ofcourse be appreciated that the ammonia- or amine-sensitive material mayhave different optical properties to those mentioned in the exampleabove, such that the range of optical wavelengths it passes in theabsence of ammonia or amine and the range of optical wavelengths itpasses in the presence of ammonia or amine may be different, and a lightsource having a peak wavelength that falls within the pass band of theammonia- or amine-sensitive material in the absence of ammonia butoutside of the pass band of the ammonia- or amine-sensitive material inthe presence of ammonia or amine should be used.

An alternative embodiment of an ammonia or amine sensor 44 isillustrated in FIG. 5, in which the same reference numerals are used toidentify elements common to the embodiments of FIGS. 4 and 5. In thisembodiment a second LED 78 which emits blue light having a peakwavelength in the 432 nm range, that is to say within the main pass bandof the ammonia- or amine-sensitive material in the presence of ammoniaor amine, is provided as well as the LED 70 which emits orange lighthaving a peak wavelength in the 602 nm range.

The LEDs 70, 78 are arranged such that light emitted by both LEDs 70, 78is incident on the photodetector 72. An ammonia- or amine-sensitivematerial 74, which in this example is an ammonia- or amine-sensitive dyefilm consisting of a pH-sensitive dye such as bromophenol blue in a solutionmixed with polyvinylpyrrolidone, is disposed between the LED 70and the detecting surface of the photodiode 72. The ammonia- oramine-sensitive dye film 74 may be deposited directly onto the LEDs 70,78 or the detecting surface of the photodiode 72 or both, or may beprovided on a substantially transparent medium 76 disposed in an opticalpath between the LEDs 70, 78 and the detecting surface of the photodiode72.

In operation of this embodiment of the ammonia or amine sensor 44, acontrol signal is received from the control system 54 to actuate theLEDs alternately, to ensure that there is no crosstalk between the twoLEDs 70, 78. The photodetector 72 detects the light from the LEDs 70, 78and outputs a voltage in the range 0 to 2.5V, which output voltage isdependent on the intensity of light received by the photodetector 72. Inthis embodiment, when no ammonia or amine is present, the ammonia- oramine-sensitive material 74 has a peak transmittivity at 606 nm, andthus allows substantially all the light from the orange LED 70 to passto the photodetector 72, whilst reducing the intensity of the lightreceived from the blue LED 78 by the photodetector 72. Thus, in theabsence of ammonia or amine the photodetector 72 produces a high outputvoltage (e.g. at or close to 2.5V) when the orange LED 70 is actuated.Where ammonia or amine is present, the optical transmittivity of theammonia- or amine-sensitive material 74 is reduced, having in thisexample a peak transmittivity at 432 nm. Thus, the intensity of lightreceived from the orange LED 70 by the photodetector 72 is reduced,whilst the intensity of light received by the photodetector 72 from theblue LED 78 increases. Thus, in the presence of ammonia thephotodetector 72 produces a high output voltage when the blue LED 78 isactuated. The output of the photodetector 72 is measured by theprocessor 20 in synchronisation with the alternate actuation of theorange and blue LEDs 70, 72 to provide an indication of the opticaltransmittivity of the ammonia- or amine-sensitive dye film 74 when eachof the LEDs 70, 78 is actuated. This allows the optical transmittivityof the ammonia- or amine-sensitive dye film 74 to be measured for bothlight sources 70, 78 using a single photodetector 72. This embodiment ofthe ammonia or amine sensor 44 is advantageous in that it is able toprovide an indication of a fault with the sensor 44. If thephotodetector 72 were partially obscured or darkened for a reason otherthan exposure to ammonia or amine the transmittivity of the ammonia- oramine-sensitive dye film 74 would decrease at both the 602 nm and the432 nm wavelength (i.e. when either LED 70, 78 is actuated). Thus, if adecrease in the optical transmittivity of the ammonia- oramine-sensitive film 74, as indicated by the output voltage of thephotodetector 72 when each of the LEDs 70, 78 is actuated, is detectedfor both wavelengths it is clear that there is a fault with the sensor44.

Instead of providing only a single photodetector 72, the alternativeammonia or amine sensor 44 may be provided with a second photodetectorin a sealed light-tight chamber with the blue LED 78. However, thisarrangement requires further splitting of the flow of volatilecompounds, which is undesirable for the reasons explained above.

FIG. 6 is a schematic illustration of a heated metal oxide sensor 48, 50used in the detection stage 18. The heated metal oxide sensor 48, 50comprises a substrate 80 of alumina having on one side thereof aplatinum heater 82, which is shown in dashed lines in FIG. 5. Aplurality (six in this example) of interdigitated gold electrodes 84 isprovided on the other side of the substrate 80, and a sensor film isapplied to the interdigitated gold electrodes 84. The sensor film inthis example is made from a paste made up of equal masses of zinc oxideand tin oxide powders mixed in water. Alternatively the sensor film canbe made from a paste comprising a non-aqueous binder with equal massesof zinc oxide and tin oxide.

A current is applied to the platinum heater 82 to achieve a desiredoperating temperature of the sensors 48, 50. In the example shown inFIGS. 2 and 3, one of the metal oxide sensors 48, 50 operates at atemperature of 400° C., whilst the other metal oxide sensor 50, 48operates at a temperature of 450° C. The operating temperature of themetal oxide sensors 48, 50 determines their selectivity and sensitivityto volatile compounds, and these operating temperatures have been foundto provide a suitable level of selectivity and sensitivity. The controlsystem 24 controls the operating temperature of the metal oxide sensors48, 50 by measuring the resistance of the platinum heater 82 andcomparing the measured resistance of the platinum heater 82 with theresistance required at the desired temperature (by consulting a look-uptable, for example) and adjusting the current applied to the platinumheater 82 to achieve the required resistance and hence the desiredtemperature.

In use of the heated metal oxide sensor 48, 50, a voltage is applied tothe sensor film and the current flowing through the sensor film ismeasured. The current flowing through the sensor film varies dependingupon the presence of volatile compounds. Thus, a particular measuredcurrent may be interpreted as being indicative of the presence of aparticular volatile compound.

The measured current in the metal oxide sensors 48, 50 is converted intoa voltage between 0 and 2.5V and this voltage is passed to the processor20 (with or without pre-processing), which processes this output signalin conjunction with signals output by the other sensors 46, 48 todetermine if volatile compounds present in the gas derived from thesample supplied by the patient are indicative of disease.

The metal oxide sensors 48, 50 are operable at temperatures as low as150° C., and the control system 24 is able to control their operatingtemperatures according to the application for which the apparatus 10 isto be used. For example, the metal oxide sensors 48, 50 may be mostsensitive to volatile compounds which are indicative of a particulardisease at one operating temperature, and most sensitive to othervolatile compounds that are indicative of a different disease at adifferent operating temperature. By changing the operating temperatureof the metal oxide sensors 48, 50 according to the particularapplication for which the apparatus 10 is to be used, diagnosticaccuracy and effectiveness can be optimised. The applicant has found,however, that at temperatures of 300° C. and below the response of themetal oxide sensors 48, 50 can be masked due to the presence of watereluting from the separating stage 16, which can cause smaller outputsignals which may be indicative of the presence of particular volatilecompounds to be obscured. Temperatures in the range 400° C. to 500° C.have been found to be particularly suitable.

It will be appreciated that the sensors 44, 46, 48, 50 have a finitelifespan. It is anticipated that the sensors 44, 46, 48, 50 used in theembodiments described herein will have a lifespan in excess of one year,but this will depend to some extent upon the number of diagnosingoperations performed by the apparatus 10. To facilitate maintenance ofthe apparatus 10 the sensors 44, 46, 48, 50 may be implemented asseparate modules which can be replaced individually when necessary. Tothis end, the sensor modules may be provided with quick-releaseconnectors by means of which they can be connected to and disconnectedfrom the detection stage 18.

The processor 20 may include an analogue to digital converter (ADC) toconvert the voltages supplied by the outputs of the sensors 44, 46, 48,50 into digital signals which can be used by the processor.Alternatively, the sensor output signals may be pre-processed to convertthe output voltages into a digital format.

The processor 20 is programmed to produce, from the outputs of thesensors 44, 46, 48, 50, a trace of voltage, current or resistance versustime for each of the sensors 44, 46, 48, 50. These traces include peaksat certain times (as different volatile compounds elute from theseparating stage 16 at different times), indicating when particularvolatile compounds were detected by the sensors 44, 46, 48, 50. In oneembodiment, the processor 20 compares these traces to known traces orprofiles from one or more previously-diagnosed samples containingparticular volatile compounds or combinations of volatile compoundswhich are indicative of particular diseases. If the processor 20identifies a correlation between the traces produced from a currentsample and those associated with a particular disease, a diagnosis ofthat disease can be made and displayed on the display 22. As isdiscussed above, a suggested treatment plan may be associated with eachdisease such that when the processor 20 identifies a correlation orother relationship between the traces produced by the current sample andthose associated with the disease and diagnoses the disease a suggestedtreatment plan can be displayed with the diagnosis, to allow a healthprofessional to begin treatment of the patient without delay.

In another embodiment, an artificial neural network (ANN) is used toanalyse the traces produced from the output of the sensors 44, 46, 48,50 to diagnose particular diseases based on the volatile compoundsdetected by the sensors 44, 46, 48, 50 of the detection stage 18.

FIG. 7 is a flow diagram illustrating a process for acquiring data totrain the ANN used to analyse the traces to diagnose disease based onthe volatile compounds detected by the sensors 44, 46, 48, 50 of thedetection stage 18.

At step 90 initial parameters for a data acquisition run are set. Theseparameters include the sample name, the duration of the data acquisitionrun, and the data acquisition rate. The data acquisition rate can be setat any appropriate value, but it has been found that a rate of betweentwo and five readings or data samples per second gives rise to goodresults, as unresolved or overlapping peaks representing the differentvolatile compounds emerging from the separating stage 16 can beobserved, as will be explained in more detail below.

At step 92 a patient sample is injected into the gas chromatography oven12 and a marker is placed on the traces produced by the processor 20from the outputs of the sensors 44, 46, 48, 50 so that the time at whichthe sample was injected can be identified.

At step 94 a chromatogram is acquired from the detection stage 18 as aseries of data points, each of which represents an output of a sensor44, 46, 48, 50. With a data acquisition rate of 2 readings or datasamples per second 2 data points of the chromatogram are produced ineach one second interval. These data points are saved at step 96, and atest is made at step 98 to determine whether another sample is to beinjected to the apparatus 10. If a further sample is to injectedprocessing returns to step 90, whilst if no further sample is to beinjected the process stops at step 99.

In a typical data acquisition run of 30 minutes, 3600 data points willbe collected (120 samples/minute×30 minutes). To simplify the input ofthe saved data to the ANN, the number of data points is reduced bydividing the time axis of the chromatogram into regular time intervalsT, which may be, for example, 15 seconds long. The processedchromatogram data is summed over each of these intervals. One timeinterval containing the summed values is referred to as a ‘bin’. FIG. 8is a flow diagram illustrating a process used to produce the bins.

At step 100 the bin parameters, such as the number bins N, the width ofeach bin (e.g. a 15 second bin or a 20 second bin with a 5 secondoverlap), the bin height (which is a threshold below which data can bedisregarded as noise) and a bin area threshold are defined andinitialised.

At step 102 the chromatogram is acquired, and a noise reductionalgorithm is applied at step 104. The first differential of thechromatogram with respect to time, dR/dt (where R is the resistance thesensor at time t) is taken at step 106, by subtracting the value of eachdata point by that of the preceding data point and dividing the resultby the time interval between the data point and the preceding datapoint, and a loop counter i is initiated at step 108. A processing loopis then entered at step 110 in which a bin value for each bin iscalculated by summing the differentiated chromatogram data points abovethe preset height threshold within the time of the respective bins. Ifthe calculated bin has an area less than the preset threshold its valueis set to 0. This process is repeated for all N data bins.

Once all of the bins have been calculated, they are normalised at step120 and the normalised bin data is saved at step 122.

FIG. 9 is a flow diagram illustrating a training process for the ANN. Atstep 130 training parameters for the ANN are initialised. For example, asensor may be selected, the bin threshold values and the number andwidth of the data bins (discussed above) are input. An output format(e.g. diagnosis of a particular disease or identification of all diseasetypes) is selected. The location of training data is identified andparameters of the neural network are selected.

The training process begins at step 132 and a first data file containingtraining data is loaded at step 134. The data bins are loaded at step138 and the bin data is propagated through the network engine at step140. At step 142 the output from the ANN is compared to a requiredoutput and error values are generated. Error correction factors arecalculated at step 144 and are propagated back through the ANN at step146 by adjusting weightings. At step 148 a test is made to determinewhether the data file currently being processed is the last data file.If not, the next data file is read (at step 136) and processing returnsto step 138. If the current data file is the last data file, a totalerror for the training set is calculated at step 150. At step 152 a testis made to determine whether the total error is below a threshold. Ifnot the processing returns to step 134 and the training data isre-entered. If the total error is below the threshold the network issaved at step 154 and can subsequently be used by the apparatus 10 forreal-time diagnosis of disease from a sample of a bodily fluid providedby a patient.

FIG. 10 is a flow diagram illustrating the process used by the ANN ofthe apparatus 10 to make a real-time diagnosis of a disease. The processstarts at step 160, and at step 162 an appropriate ANN is selected andloaded. Run parameters including sample name and run time are set atstep 164. A sample is injected into the gas chromatography oven 12 atstep 166, and a chromatogram is acquired by the sensors 44, 46, 48, 50of the detection stage 18 in step 168. The data is saved at step 170 andis loaded into data bins at step 172. The bin data is propagated throughthe entire ANN at step 174 and a decision is produced by the ANN, toproduce a diagnosis, which is displayed on the display 22 of theapparatus 10 and saved at step 176. A test is made at step 178 todetermine whether another sample is to be analysed. If not, the processends at step 182. If another sample is to be analysed, a test is made atstep 180 to determine whether the same ANN is to be used. If so,processing recommences at step 164. If not, a new ANN is selected andloaded at step 162 before processing recommences at step 164.

It will be appreciated that other pattern recognition methodologies maybe used in the apparatus 10 instead of or alongside an ANN to diagnosedisease on the basis of volatile compounds detected by the detectionstage 18. For example, the processor could implement a canonical,Fourier transformation, wavelet transformation, Bayesian, principalcomponent analysis (PCA), or k-nearest neighbour (KNN) patternrecognition algorithm, or a statistical fixed algorithm, threshold orBoolean algorithm.

The apparatus 10 can be used to diagnose gastro-intestinal disease suchas C. difficile, Norovirus, Campylobacter, Salmonella and the like bytaking a stool sample from a patient, analysing it to identify volatilecompounds contained in the gas derived from the sample and using thecomparison method or the ANN method described above.

The apparatus 10 can also be used to diagnose other conditions byanalysing other bodily fluid. For example, urine samples can be analysedto diagnose renal disease and other conditions, whilst breath samplescan be analysed to diagnose lung disease.

Whilst the apparatus 10 of the present invention is described as havingfour sensors 44, 46, 48, 50, it will be appreciated that more, fewer, ordifferent combinations of sensors can be used. For example, in a devicedesigned solely to diagnose C. difficile by analysing gas derived fromstool samples provided by patients, only a combination of an ammonia oramine sensor 46 and a single heated metal oxide sensor 48, 50 may beprovided. Where a combination of two or more sensors including a heatedmetal oxide sensor 48, 50 is used, it is preferred that the sensors arearranged in a serial configuration, with the heated metal oxidesensor(s) 48, 50 being provided as the final sensor(s) in the series, toincrease the likelihood that any viruses or other microbiologicalcontaminants present in the volatile compounds produced by theseparating stage will be destroyed prior to being expelled from thedetection stage 18 through the exhaust port.

The apparatus 10 may be provided as an integrated multi-purpose devicewhich is capable of analysing different types of sample to diagnose avariety of different conditions. For example, in one mode the apparatus10 may be configured to diagnose a gastrointestinal condition such as C.difficile by analysing a stool sample. In a second mode the apparatus 10may be configured to analyse a stool sample to diagnose a range ofgastrointestinal conditions such as C. difficile, ulcerative colitis,colorectal cancer or the like. In a third mode of operation, theapparatus 10 may be configured to analyse a urine sample to diagnoseprostate cancer.

It will be appreciated that different conditions are likely to producedifferent traces or profiles of compounds in samples, and thus in orderto produce a diagnosis quickly the apparatus 10 may store a plurality ofknown traces or profiles from previously-diagnosed samples containingparticular volatile compounds or combinations of volatile compoundswhich are indicative of particular diseases in the storage 26. Anappropriate trace or profile can be recalled when necessary for aparticular type of analysis or diagnosis. In the example described abovea first trace or profile from a sample taken from a patient previouslydiagnosed as having C. difficile may be stored in the storage device 26,along with a second trace or profile which may be a composite trace oran amalgamation of traces or profiles from samples taken from patientspreviously diagnosed as having a range of conditions such as C.difficile, ulcerative colitis, colorectal cancer and the like. A thirdtrace or profile from a sample taken from a patient previously diagnosedas having prostate cancer may also be stored in the storage device 26.Thus, an appropriate trace or profile may be selected according to theanalysis or diagnosis being performed, with the trace produced by thesample from the patient being compared to the stored trace to produce adiagnosis.

Additionally or alternatively, different ANNs may be stored in thestorage device 26 or in memory of the processor 20 or the control system24, with each ANN being trained to diagnose a particular condition orrange of conditions. This, an appropriate ANN may be selected accordingto the analysis or diagnosis being performed.

This ability to store and recall different traces and/or ANNs gives theapparatus 10 great flexibility as it allows a single device to be usedto diagnose a range of conditions. Additionally, the apparatus 10 can beupdated with revised traces and ANNs as improved data is obtained and asnew compounds or combinations of compounds which are indicative ofparticular conditions are discovered. Thus, the apparatus 10 isexpandable to meet future diagnostic requirements.

The foregoing description presents exemplary embodiments of theapparatus of the present invention. For the sake of completeness twoprototype systems used by the applicant in the development of theapparatus of the present invention will now be described. It will beappreciated that these prototype systems also constitute embodiments ofthe apparatus of the present invention.

The first prototype apparatus comprises a gas chromatography oven withan injection port into which samples of gas collected from patientsamples, such as stool samples, can be injected. Gas is collected fromthe samples in a separate process in which the samples are heated inheadspace vials having an integrated septum through which headspace gascan be used using a gas tight syringe. The headspace gas can then beinjected directly into the gas chromatography oven.

A separation stage of this first prototype apparatus uses twocommercially-available 30 metre capillary columns with an internaldiameter of 0.32 mm an a stationary phase film thickness of 4 μm areinterfaced to the injection port using push fit glass connectors and twopieces of silica guard column, to provide four separate outlets of theseparation stage. Three of the outlets are interfaced to a sensor array(which is described in detail below), whilst the fourth is interfaced toa conventional flame ionisation detector (FID) which is integral to thegas chromatography oven. A pump is provided to pump blended dry cylinderair around this system to transport the gas collected from the samples.

Two types of sensor are used in the sensor array of this system. Thefirst is a metal oxide sensor of the type described in detail above, andthe second is an ammonia or amine sensor of the type described in detailabove. Two metal oxide sensors are used in the sensor array, with asingle ammonia or amine sensor. The ammonia or amine sensor is encasedin a light-tight outer casing before being integrated into the sensorarray, to ensure that light from sources other than the LED is excludedand thus cannot influence the output of the ammonia or amine sensor.

The sensors are controlled and their signals conditioned by a bespokecontrol circuit. A hardware feedback loop is provided which maintainsthe temperature of each of the metal oxide sensors at a predefined valueregardless of heat losses. The temperature of the heater of each metaloxide sensor is continually monitored by measuring voltages permittingthe resistance of the platinum heater to be calculated, from which thetemperature can be derived. Signals output by the sensors (resistancechange for heated metal oxide sensors and voltage change for the ammoniaor amine sensor) and the sensor heater voltages are conditioned usingconditioning circuitry and fed via an analogue to digital converter viaa USB connection to a personal computer running custom diagnosissoftware.

The software provides a scrolling display of resistance or voltageversus time, and a constant update of the sensor temperatures. When asample is injected a marker with sample information is added to thetrace. This marker is used as a ‘time zero’ reference point for thesubsequent calculation of retention time values (see below). Theinformation is saved in a unique file.

Data collected from each sensor from a sample are transformed so thatthe change in resistance with time (dRidt) is displayed, in order tofacilitate the deconvolution of peaks that elute with similar retentiontimes. Smoothing is also applied to the traces to reduce the effects ofelectrical noise and a height threshold is applied in order to excludesmall baseline fluctuations caused by noise. Data files are saved inMicrosoft Excel format. The files contain a series of retention timevalues (the time taken for specific compounds to elute the columns) andthe respective peak area for each sensor. These data files are collectedand analysed for five stool types—Campylobacter, C. difficile, normal(asymptomatic individuals), Salmonella and undiagnosed. The softwareincludes an Artificial Neural Network (ANN) for performing thisdiagnosis.

Input to the ANN is accomplished by dividing the 30 minute (1800 second)time-span of each data acquisition run into 120 consecutive fifteensecond segments ('bins'), and integrating the peaks of thedifferentiated display across each bin, thus creating an array of 120input bins. The bins are then normalised proportionally such that thelargest bin equals 1. The bins then contain a normalised representationof the chromatogram. The software includes an option to make the binswider so that the bins can overlap by either 5 or 15 seconds, in casepeaks span adjacent bins.

The output from the ANN can be selected to have just two channels (e.g.C. difficile/Not C. difficile), or six channels, one for each diseasetype plus one for calibration data produced using a sample of ethanol.

In experimental use of the first prototype system, a proportion of thedata files was used to train the ANN (training set), and validation ofthe ANN was undertaken using the remaining data (validation set), as isshown in Table 1 below.

TABLE 1 Numbers of each disease type in the training and validationsets. Type Training set Validation set Normal 22 11 C. difficile 22 10Campylobacter 23 9 Ethanol 7 4 Salmonella 15 5 Undiagnosed 7 5 Total 9644

In addition to the 120 input bins (input layer of 120 units) and the 2or 6 outputs (output layer of either 2 or 6 units), the ANN has one ormore hidden layers. The number of units in each hidden layer can affectthe accuracy of the artificial neural network. As it is not possible topredict the optimum number of units in a hidden layer, it is necessaryto test as many combinations as possible. To this end, the softwarepermits the user to automatically create and validate ANNs where thenumber of units in the hidden layer before the output layer (which isthe only hidden layer in single hidden layer networks) is decrementedfrom 120 down to the number of outputs. This was carried out for singlehidden layer ANNs using all combinations of the following parameters:height threshold of 0 or 50; area threshold of 0 or 200; 2 outputs or 6outputs; bin overlaps of 0, 5 or 15 seconds; using either normalised binareas or binary transformed bin areas (1 if bin area >0, 0 otherwise).

The best ANNs were found to have the following parameters: heightthreshold=50; area threshold=200; bin overlap=15 seconds; normalised binareas.

Using these parameters the results for the 6-output network are shown inTable 2, and the results for the 2-output network are shown in Table 3.

TABLE 2 Results from the 6-output network Type Matched Total % CorrectNormal 8 11 72.7 C. difficile 8 10 80.0 Campylobacter 4 9 44.4 Ethanol 44 100.0 Salmonella 2 5 40.0 Undiagnosed 4 5 80.0 Total 30 44 68.2

TABLE 3 Results from the 2-output network Type Matched Total % CorrectNot C. difficile 29 34 85.3 C. difficile 8 10 80.0 Total 37 44 84.1

Since Salmonella and Campylobacter samples are rarely encountered in ahospital setting, the differentiation of patients with C. difficile frompatients with either diarrhoea of unknown aetiology or patients who maybe asymptomatic is most important in relation to the proposed use of theprototype. Therefore, the ANN was trained using the same data set asbefore (Table 1), but with the ethanol, Campylobacter and Salmonellasamples removed from the training and validation sets. These results areshown in Tables 4 and 5.

TABLE 4 Results from the 2 output network excluding ethanol, Salmonellaand Campylobacter samples from training and validation sets. Matched No.in validation set % Correct Not C. difficile 18 18 100.0 C. difficile 810 80.0 Total 26 28 92.9

TABLE 5 Results from the 6 output network excluding ethanol, Salmonellaand Campylobacter samples from the training and validation sets. MatchedNo. in validation set % Correct Normal 9 10 90.0 C. difficile 9 10 90.0Undiag 5 5 100.0 Total 23 25 92.0

For comparison, neural networks were generated using data obtained froma conventional FID detector, gathered at the same time as the resultsfrom the GC Detector unit. The results are shown in Tables 6 and 7 forthe 6-output and 2-output networks respectively.

TABLE 6 Results from the 6-output network using data from a FID detectorType Matched Total % Correct Normal 7 10 70.0 C. difficile 4 10 40.0Campylobacter 1 9 11.1 Ethanol 3 4 75.0 Salmonella 1 6 16.7 Undiagnosed4 5 80.0 Total 20 44 45.5

TABLE 7 Results from the 2-output network using data from a FID detectorType Matched Total % Correct Not C. difficile 25 34 73.5 C. difficile 410 40.0 Total 29 44 65.9

As can be seen from the tables above, networks generated using data fromthe prototype system give more accurate decisions than networksgenerated using the FID data.

The second prototype system is similar to the first, but uses acommercially available multicapillary column of 50 cm in length andhaving 1200 capillaries of 4 μm internal diameter and a stationary phasefilm thickness of 0.2 μm in the separation stage. A pump is provided topump laboratory air into the multicapillary column to transport the gascollected from the samples, with the laboratory air being filtered bycharcoal filters to remove any contaminants from the laboratory airprior to entry into the multicapillary column. An outlet of themulticapillary column is interfaced directly to a heated metal oxidesensor of the type described above, which operates at a temperature of450° C.

The heated metal oxide sensor is controlled and its signals conditionedby a bespoke control circuit. A hardware feedback loop is provided whichmaintains the temperature of the metal oxide sensor at a predefinedvalue regardless of heat losses. The temperature of the heater of themetal oxide sensor is continually monitored by measuring voltagespermitting the resistance of the platinum heater to be calculated, fromwhich the temperature can be derived. The resistance change signaloutput by the sensor and the sensor heater voltage is conditioned usingconditioning circuitry and fed via an analogue to digital converter viaa USB connection to a personal computer running custom diagnosissoftware.

The software provides a scrolling display of resistance or voltageversus time, and a constant update of the sensor temperatures. When asample is injected a marker with sample information is added to thetrace. This marker is used as a ‘time zero’ reference point for thesubsequent calculation of retention time values (see below). Theinformation is saved in a unique file.

Data collected from the sensor from a sample are transformed so that thechange in resistance with time (dR/dt) is displayed, in order tofacilitate the deconvolution of peaks that elute with similar retentiontimes. Smoothing is also applied to the traces to reduce the effects ofelectrical noise and a height threshold is applied in order to excludesmall baseline fluctuations caused by noise. Data files are saved inMicrosoft Excel format. The files contain a series of retention timevalues (the time taken for specific compounds to elute the columns) andthe respective peak area for each sensor. These data files are collectedand analysed for four stool types—Campylobacter, C. difficile, normal(asymptomatic individuals) and undiagnosed. The software includes anArtificial Neural Network (ANN) for performing this diagnosis.

Input to the ANN is accomplished by dividing the 10 minute (600 second)time-span of each data acquisition run into 40 consecutive fifteensecond segments ('bins'), and integrating the peaks of thedifferentiated display across each bin, thus creating an array of 40input bins. The bins are then normalised proportionally such that thelargest bin equals 1. The bins then contain a normalised representationof the chromatogram. The software includes an option to make the binswider so that the bins can overlap by either 5 or 15 seconds, in casepeaks span adjacent bins.

The output from the ANN can be selected to have just two channels (C.difficile/Not C. difficile), or five channels, one for each disease typeplus one for calibration data produced using an ethanol sample.

In experimental use of the second prototype a proportion of the datafiles was used to train the ANN (training set); validation of the ANNwas undertaken using the remaining data (validation set), as shown inTable 8 below.

TABLE 8 Samples used in the training and validation of the secondprototype with multicapillary column Sample Total Training setValidation set Normal 14 7 7 C. difficile 26 13 13 Campylobacter 11 5 6Ethanol 6 3 3 Salmonella 0 0 0 Undiagnosed 7 3 4 Total 64 31 33

Table 9 below shows the results obtained with a 2 output ANN (C.difficile/Not C. difficile) with either 1 or 2 hidden layers. Theoverall correct classification of samples was 82% if 2 hidden layerswere utilised in the ANN. This compares to an overall classification ofonly 73% if 1 hidden layer was utilised. These results demonstrate thatsecond prototype utilising a short microcapillary column is able todeliver disease diagnosis results in 10 minutes.

TABLE 9 The classification of the validation set using a 2 output ANNwith either 1 or 2 hidden layers. No. units in Hidden % ANN detailsLayer Type Matched Total Correct One hidden layer 70 Not 14 20 70.0 40 ×15 sec C. difficile Bins, No overlap, C. difficile 10 13 76.9 Threshold50, Total 24 33 72.7 Area Threshold 0 Two hidden layers 70, 107 Not 1720 85.0 40 × 15 sec C. difficile Bin No overlap, C. difficile 10 13 76.9Threshold 50 Total 27 33 81.8 Area Threshold 0

Exemplary applications of the apparatus 10 will now be described withreference to the results of experiments carried out by the presentapplicant using a prototype of the apparatus 10.

In a first experiment, the prototype was used to diagnose C. difficilefrom a stool sample provided by a patient. In this experiment healthystool samples and stool samples from patients previously diagnosed withC. difficile and with ulcerative colitis were analysed using theprototype apparatus and volatile compounds present in each of the sampletypes were collated and subjected to a discriminant analysis, theresults of which are shown in the graph of FIG. 11. It can clearly beseen from this graph that the compounds found in stool samples frompatients diagnosed with C. difficile fall into a distinct grouping, andfrom this information the apparatus 10 can be configured to diagnose C.difficile by analysing stool samples, either by direct comparison with aknown profile for stool containing compounds indicative of C. difficile,or by implementing an ANN trained with data collected from samples takenfrom patient diagnosed with C. difficile.

In a second experiment, the prototype used in the first experiment wasused to analyse urine samples provided by two healthy volunteers toproduce traces indicative of different volatile compounds found in thesamples. Gas was evolved from the samples and passed through thedetection stage to produce the traces.

Two methods were used to extract volatile compounds from the gas evolvedfrom the urine samples. In the first method air was extracted from aheadspace above the urine sample, whereas in the second method an SPMEfibre was exposed to the headspace above the urine sample and wassubsequently inserted into the injection port of the gas chromatographyoven for desorption and subsequent analysis. In both methods the urinesamples were heated to approximately 60° C. prior to the extraction ofthe headspace air or exposure of the SPME fibre to the headspace air, topromote the release of volatile compounds from the urine samples. Thegas chromatography oven was pre-heated to a temperature of 30° C., whichtemperature was held for six minutes following the injection of theheadspace air or SPME fibre into the oven. The temperature of the gaschromatography oven was subsequently raised by 5° C. per minute until afinal temperature of 100° C. was reached. The temperature was held at100° C. for 40 minutes, giving a total run time of 60 minutes. In bothmethods, three samples were analysed in parallel, with a first 6 mlurine sample being acidified with 1 ml of sulphuric acid (1M), second 6ml urine sample being basified with 0.5 ml of sodium hydroxide (0.5M)and a third 6 ml sample being treated with an equivalent quantity ofdeionised water. The acidified and basified samples produced morevolatile compounds than the untreated sample.

Table 10 below shows the results of the analysis of the urine samplesfrom the healthy volunteers HV1 and HV2 using the first method. It willbe noted that volatile compounds were detected by the detection stage 18at 45 distinct retention times, indicating the presence in the urinesamples of up to 45 volatile compounds. Additionally, more volatilecompounds were detected for the acidified samples than for the basifiedsamples and the untreated samples.

TABLE 10 HV1 acid- HV2 RT untreated ified basified untreated acidifiedbasified 1 0.92 x 2 1.07 x x x 3 1.14 x x x 4 1.60 x x x x 5 1.80 x x x6 2.28 x x 7 4.54 x 8 4.85 x x x 9 5.12 x x 10 5.24 x 11 5.44 x 12 5.80x 13 6.04 x 14 6.57 x 15 8.50 x x x 16 9.20 x 17 9.75 x x x 18 10.72 x x19 10.84 x 20 11.61 x 21 13.14 x x x x 22 13.79 x x 23 14.74 x 24 15.56x 25 15.99 x x x 26 16.05 x 27 16.32 x 28 17.02 x x x 29 18.10 x 3018.50 x 31 19.29 x x 32 19.66 x x x 33 21.47 x x 34 22.79 x 35 23.44 x x36 23.98 x 37 24.42 x x 38 24.69 x 39 25.57 x 40 27.06 x 41 29.39 x x 4236.24 x 43 37.96 x 44 58.80 x 45 59.61 x Total 20 29 13 4 9 3

Table 11 below shows the results of the analysis of the urine samplesprovided by the healthy volunteers HV1 and HV2 using the second method.With this method volatile compounds were detected by the detection stage18 at 90 distinct retention times, indicating the presence in the urinesamples of up to 90 volatile compounds. As before, more volatilecompounds were detected for the acidified samples than for the basifiedsamples and the untreated samples, as is illustrated in FIGS. 12 and 13,which are screenshots showing the results of the analysis using thesecond method of the untreated urine sample and the acidified urinesample respectively.

TABLE 11 HV1 acid- HV2 RT untreated ified basified untreated acidifiedbasified 1 1.12 x x x x x x 2 1.22 x x 3 1.58 x x x x x x 4 1.96 x 52.32 x x 6 2.54 x x 7 3.02 x 8 3.23 x 9 4.07 x x 10 4.40 x 11 4.80 x x12 5.19 x x 13 6.00 x x 14 6.29 x 15 6.90 x 16 8.24 x 17 8.39 x 18 8.85x x x 19 9.11 x x 20 10.74 x x x x 21 11.60 x 22 12.13 x 23 12.77 x 2412.95 x 25 13.11 x x x x 26 13.38 x 27 13.80 x x x 28 14.09 x 29 14.24 x30 14.74 x x 31 15.24 x 32 15.57 x 33 15.71 x 34 15.94 x x 35 16.15 x xx 36 16.33 x x 37 16.83 x 38 17.78 x 39 18.55 x x x 40 18.78 x 41 18.97x 42 19.33 x x 43 19.73 x x x x 44 20.35 x x x x 45 21.12 x 46 21.48 x x47 22.70 x x x 48 23.19 x 49 23.38 x 50 23.78 x x 51 24.46 x x x 5224.73 x 53 25.45 x x 54 25.69 x 55 26.16 x x 56 26.93 x 57 27.17 x 5827.26 x 59 27.48 x 60 28.04 x x 61 29.32 x 62 29.60 x x x x 63 30.25 x xx x 64 32.17 x x 65 33.18 x 66 34.52 x x x 67 34.90 x 68 34.94 x 6935.32 x 70 36.30 x x 71 36.60 x 72 37.30 x 73 38.07 x 74 38.45 x 7539.11 x 76 40.33 x 77 40.97 x 78 41.28 x 79 44.43 x 80 45.83 x 81 46.33x 82 47.43 x 83 47.80 x 84 48.17 x 85 50.23 x 86 52.66 x 87 55.19 x x 8856.70 x x 89 59.54 x x x 90 61.24 x x x Total 26 53 32 7 18 21

These results demonstrate that the apparatus 10 can be used to diagnosedisease by analysing urine samples to detect compounds which may beindicative of disease, in the same manner as is described above inrelation to the detection of disease by analysing stool samples.

In a further experiment, the prototype used in the first two experimentswas used to analyse 52 urine samples, 17 of which were taken frompatients diagnosed by biopsy with prostate cancer, and the remaining 35of which were taken from patients where biopsy results for prostatecancer were negative.

In this experiment the ANN was trained using 11 urine samples frompatients diagnosed by biopsy with prostate cancer and 22 samples wherebiopsy results for prostate cancer were negative to provide trainingdata. After training of the ANN the 52 samples were analysed using themethod described below.

For each sample an aliquot of fresh urine, 6 ml, was transferred to aheadspace vial and was treated with 1 ml of sulphuric acid (1M). Themixture was left to equilibrate at room temperature for 30 minutes andwas thereafter heated for 20 minutes at 60° C. before extraction of 2cm³ of headspace air. Following extraction the headspace air wasimmediately injected into the injection port (100° C.) of the gaschromatography oven 12 for analysis.

The gas chromatography oven 12 was started at a temperature of 30° C.and held for 6 minutes. Then a ramp of 5° C. per minute was applieduntil a final temperature of 100° C. was reached. The temperature washeld at 100° C. for 40 minutes giving a total run time of 60 minutes.

Increasing the temperature of the oven increases the number of peaksdetected by the sensor system. This step is necessary for detectingvolatile compounds in urine as the concentrations are lower in stoolsamples.

The ANN used in this experiment gave 83% positive identification ofprostate cancer samples and 69% positive identification of negativesamples. Overall classification of samples was 74%.

1. A diagnostic apparatus for analysing a sample to diagnose disease,the apparatus comprising: a separating element for separating gasderived from the sample into component parts; a sensor arrangementcoupled to the separating element such that a component part of the gasis directed towards the sensor arrangement, the sensor arrangement beingconfigured to detect compounds which may be indicative of disease; and aprocessing element coupled to an output of the sensor arrangement, theprocessing element being configured to process a signal output by thesensor arrangement to provide a diagnosis.
 2. A diagnostic apparatusaccording to claim 1 wherein the separating element comprises amulti-capillary column.
 3. A diagnostic apparatus according to claim 1wherein the separating element may comprises a single-capillary columnor a plurality of single-capillary columns.
 4. A diagnostic apparatusaccording to claim 1 wherein the sensor arrangement comprises one ormore sensors selected from the group comprising a metal-oxide sensor, aUV sensor and an ammonia or amine sensor.
 5. A diagnostic apparatusaccording to claim 1 wherein the sensor arrangement comprises two ormore sensors arranged in a serial configuration.
 6. A diagnosticapparatus according to claim 1 wherein the sensor arrangement comprisestwo or more sensors arranged in a parallel configuration.
 7. Adiagnostic apparatus according to claim 1 wherein the sensor arrangementis configured to detect one or more volatile compounds present in thegas.
 8. A diagnostic apparatus according to claim 1 wherein the sensorarrangement is configured to detect one or more volatile organiccompounds present in the gas.
 9. A diagnostic apparatus according toclaim 7 wherein the sensor arrangement is configured to generate asignal indicative of the elution time of a volatile compound in thesample.
 10. A diagnostic apparatus according to claim 9 wherein theprocessing element is configured to compare the signal generated by thesensor arrangement to a known profile from one or morepreviously-diagnosed samples.
 11. A diagnostic apparatus according toclaim 1 further comprising a pre-treatment stage for alteringphysio-chemical parameters of the sample.
 12. A diagnostic apparatusaccording to claim 1 further comprising heating means for heating thesample to promote the release of the gas.
 13. A diagnostic apparatusaccording to claim 1 further comprising means for acidifying orbasifying the sample to alter the number or concentration of volatilecompounds detected by sensor arrangement.
 14. A diagnostic apparatusaccording to claim 1 wherein the processing element implements anartificial neural network to provide the diagnosis.
 15. A method ofdiagnosing disease by analysing a sample, the method comprising thesteps of: collecting the sample; separating a gas evolved from thesample into component parts; directing a component part of the gastowards a sensor arrangement, the sensor arrangement being configured todetect a compound which may be indicative of disease; and processing asignal output by the sensor arrangement to provide a diagnosis.
 16. Amethod according to claim 15 wherein the step of separating the gascomprises passing the gas through a separating element comprising amulti-capillary column.
 17. A method according to claim 15 wherein thestep of separating the gas comprises passing the gas through aseparating element comprising a single-capillary column or a pluralityof single-capillary columns.
 18. A method according to claim 15 whereinthe sensor arrangement comprises one or more sensors selected from thegroup comprising a metal-oxide sensor, a UV sensor and an ammonia oramine sensor.
 19. A method according to claim 15 wherein the sensorarrangement comprises two or more sensors arranged in a serialconfiguration.
 20. A method according to claim 15 wherein the sensorarrangement comprises two or more sensors arranged in a parallelconfiguration.
 21. A method according to claim 15 wherein the sensorarrangement is configured to detect one or more volatile compoundspresent in the gas.
 22. A method according to claim 15 wherein thesensor arrangement is configured to detect one or more volatile organiccompounds present in the gas.
 23. A method according to claim 21 whereinthe sensor arrangement is configured to generate a signal indicative ofthe elution time of a volatile compound in the sample.
 24. A methodaccording to claim 23 wherein the step of processing the signal outputby the sensor arrangement comprises comparing the signal generated bythe sensor arrangement to a known profile from one or morepreviously-diagnosed samples.
 25. A method according to claim 15 furthercomprising pre-treating the sample to for alter physio-chemicalparameters of the sample.
 26. A method according to claim 15 furthercomprising heating the sample to promote the release of the gas.
 27. Amethod according to claim 15 further comprising acidifying or basifyingthe sample to alter the number or concentration of volatile compoundsdetected by sensor arrangement.
 28. A method according to claim 15wherein the step of processing the signal output by the sensorarrangement comprises processing the signal using an artificial neuralnetwork to provide the diagnosis. 29.-46. (canceled)